Hepatitis C is a global health concern, with new cases being reported worldwide every year. Accurate prediction of the disease's stage is crucial in providing timely and effective treatment to patients. To achieve this, various non-invasive biochemical serum markers and clinical data have been used to identify the stage of the disease. Machine learning techniques have emerged as a powerful tool to predict the stage of this chronic liver disease without resorting to invasive biopsy procedures. In this context, an intelligent diagnostic system for Hepatitis C stage prediction has been developed using machine learning algorithms such as Artificial Neural Network (ANN), K-nearest neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression. These techniques have been shown to provide accurate predictions and avoid the side effects associated with biopsy procedures. The Hepatitis C stage prediction system is designed to analyze patient data, including clinical information and biochemical serum markers, to determine the stage of the disease. The system can provide timely and accurate predictions, allowing healthcare professionals to develop effective treatment plans for patients. In conclusion, the use of machine learning algorithms in Hepatitis C stage prediction has shown promising results, providing a non-invasive and effective alternative to traditional diagnostic methods. The presented intelligent diagnostic system using ANN, KNN, SVM, and Logistic Regression techniques can improve patient outcomes by enabling timely and effective treatment..

Hepatitis C; machine learning; Python ; Jupiter notebook.